We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized Fusion Center (FC) even when i) there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and ii) the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the Two Stage Approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. Here, the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the Adversarial Generalized Likelihood Ratio Test (A-GLRT) that uses both the reported robot measurements and trust observations to estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis simultaneously. We exploit special problem structure to show that this approach remains computationally tractable despite several unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions on a mock-up road network similar in spirit to Google Maps, subject to a Sybil attack. We extract the trust observations for each robot from actual communication signals which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT respectively.

翻译：我们针对对抗性多机器人群体感知任务中的决策制定，开发了一种鲁棒的二元假设检验框架。该框架利用机器人之间的随机信任观测值，即使在以下情况下，也能在集中式融合中心实现可处理的鲁棒决策：(i) 网络中可能存在恶意机器人，且其数量可能超过合法机器人的数量；(ii) 融合中心使用来自所有机器人的一次性噪声测量值。我们推导了两种算法来实现这一目标。第一种是两阶段方法，它基于接收到的信任观测值估计机器人的合法性，并在最坏情况恶意攻击下以可证明的方式最小化检测错误概率。在此情况下，恶意机器人的比例已知但任意。针对恶意机器人比例未知的情况，我们开发了对抗性广义似然比检验，该检验同时使用报告的机器人测量值和信任观测值来估计机器人的可信度、其报告策略以及正确假设。我们利用特殊的问题结构来证明，尽管存在多个未知问题参数，该方法仍保持计算可处理性。我们将这两种算法部署在硬件实验中，其中一组机器人对模拟道路交通状况（类似谷歌地图的精神）进行群体感知，并遭受女巫攻击。我们从实际通信信号中提取每个机器人的信任观测值，这些信号提供了发送者唯一性的统计信息。我们证明，即使恶意机器人占据多数，融合中心也能将两阶段方法和对抗性广义似然比检验的检测错误概率分别降低至30.5%和29%。